RNA seq on T-BioInfo

RNA-Seq is a technique that performs analysis of transcriptome data generated by next-generation sequencing technologies or by microarrays. A success in analysis of transcriptome is largely dependent on bioinformatics tools developed to support the different steps in the process.
The RNA-seq section of T-BioInfo provides a flexible approach to analysis of transcriptome data with a number of known and new algorithms ("modules") included and specially designed analysis features.
The analysis pipelines go across the twelve different functional sections (analysis stages) found on the interactive graph, which will process your data from start to finish by utilizing the section specific algorithms (modules). Starting from left to right these sections are:

1. Data Pre-Processing: cleaning the primers in raw reads and format transfer; Result: cleaned NGS data or array data represented as NGS pseudo-reads.
2. Data Simulation: expression of isoforms of genes is simulated; Result: artificial NGS data with introduces errors representing expression of pre-defined splice variants.
3. Error Correction: correction of sequencing errors: Result: about 75% of the sequencing errors will be corrected
4. Mapping on Genome or Genes: alignment of reads against reference genome or mRNAs; Result: alignments of reads against references
5. Exon Detection: detection of expected exons in the reference genome; Result: GTF file that annotates predicted exons in the genome
6. Mapping on exon junctions: how exons are linked in isoforms according to NGS data; Result: alignments of reads against exon junctions.
7. Isoform Construction: splice variants are generated based on found exon junctions; Result: GTF file that annotates the predicted splice variants
8. GTF file processing: merging different annotations of the genome; Result: balanced annotation of the genome based on several NGS data sets.
9. Mapping Statistics: selection of the correct mapping for a read; Result: posterior probability for a read to be generated by a specific genome site
10. Expression Table: generation of expression values for genes and isoforms: Result: table of expressions across genes and isoforms
11. Differential Expression: differential expression according to predefined contrasts between biological conditions; Result: up and down regulation of genes
12. Mining analysis results: machine learning methods and integration of results for several parallel analysis pipelines; Result: compression of results and comparison of parallel analyses.